Edge Intelligence: Advanced Deep Transfer Learning for IoT Security presents a comprehensive exploration into the critical intersection of cybersecurity, edge computing, and deep learning, offering practitioners, researchers, and cybersecurity professionals a definitive guide to protect IoT/IIoT systems. This book delves into the synergistic potential of edge computing and advanced machine/deep learning algorithms, providing insights into lightweight and resource-efficient models with a special focus on resource-constrained edge devices. The rapidly evolving nature of cyberattacks underscores the need for updated and integrated resources that address the intersection of cybersecurity, edge computing, and deep learning. The authors address this issue by offering practical insights, lightweight models, and proactive defense mechanisms tailored to the unique challenges of securing edge devices and networks. This book is not only written to provide its audience effective strategies to detect and mitigate network intrusions by leveraging edge intelligence and advanced deep transfer learning techniques but also to provide practical insights and implementation guidelines tailored to resource-constrained edge devices.
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Dr. Jawad Ahmad (SMIEEE) is a highly experienced teacher with a decade of teaching and research experience in prestigious institutes. He has taught at renowned institutions such as Edinburgh Napier University (UK) and Glasgow Caledonian University (UK) etc. He has also served as a supervisor for several PhD, MSc, and undergraduate students, providing guidance and support for their dissertations. He has published in renowned journals including IEEE Transactions, ACM Transactions, Elsevier and Springer with over 150 research papers and 4500 citations. For the past three years, his name has appeared on the list of the world's top 2% scientists in Cybersecurity, as published by Clarivate (a list endorsed by Stanford University, USA). Furthermore, in 2020, he was recognized as a Global Talent in the area of Cybersecurity by the Royal Academy of Engineering (UK). To date, he has secured research and funding grants totalling £195K. In terms of academic achievements, he has earned a Gold medal for his outstanding performance in MSc and a Bronze medal for his achievements in BSc.
Shahid Latif received the B.Sc. and M.Sc. degrees in electrical engineering from HITEC University Taxila, Pakistan, in 2013 and 2018, respectively. He is currently pursuing the Ph.D. degree with the School of Information Science and Engineering, Fudan University, Shanghai, China. From 2015 to 2019, he served as a Lecturer with the Department of Electrical Engineering, HITEC University Taxila. During his Teaching Carrier, he has supervised several projects in the field of electronics, embedded systems, control systems, and the Internet of Things. He is currently working in the research area of cybersecurity of the Industrial Internet of Things (IIoT).
Dr. Wadii Boulila received the B.Eng. degree (Hons.) in computer science from the Aviation School of Borj El Amri, in 2005, the M.Sc. degree in computer science from the National School of Computer Science (ENSI), University of Manouba, Tunisia, in 2007, and the Ph.D. degree in computer science jointly from ENSI and Telecom-Bretagne, University of Rennes 1, France, in 2012. He is currently an Associate Professor of computer science with Prince Sultan University, Saudi Arabia. He is also a Senior Researcher with the RIOTU Laboratory, Prince Sultan University; and the RIADI Laboratory, University of Manouba. Previously, he was a Senior Research Fellow with the ITI Department, University of Rennes 1. He has participated in numerous research and industrial-funded projects. His primary research interests include data science, computer vision, big data analytics, deep learning, cybersecurity, artificial intelligence, and uncertainty modeling. He is an ACM Member and a Senior Fellow of the Higher Education Academy (SFHEA), U.K. He received the Award of the Young Researcher in computer science in Tunisia for the year 2021 from Beit El-Hikma; the Award of Best Researcher from the University of Manouba, in 2021; and the Award of Most Cited Researcher at the University of Manouba, in 2022. He has served as the chair, a reviewer, and a TPC member for many leading international conferences and journals.
Edge Intelligence: Advanced Deep Transfer Learning for IoT Security presents a comprehensive exploration into the critical intersection of cybersecurity, edge computing, and deep learning, offering practitioners, researchers, and cybersecurity professionals a definitive guide to protect IoT/IIoT systems. This book delves into the synergistic potential of edge computing and advanced machine/deep learning algorithms, providing insights into lightweight and resource-efficient models with a special focus on resource-constrained edge devices. The rapidly evolving nature of cyberattacks underscores the need for updated and integrated resources that address the intersection of cybersecurity, edge computing, and deep learning. The authors address this issue by offering practical insights, lightweight models, and proactive defense mechanisms tailored to the unique challenges of securing edge devices and networks. This book is not only written to provide its audience effective strategies to detect and mitigate network intrusions by leveraging edge intelligence and advanced deep transfer learning techniques but also to provide practical insights and implementation guidelines tailored to resource-constrained edge devices.
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Paperback. Condition: new. Paperback. Edge Intelligence: Advanced Deep Transfer Learning for IoT Security presents a comprehensive exploration into the critical intersection of cybersecurity, edge computing, and deep learning, offering practitioners, researchers, and cybersecurity professionals a definitive guide to protect IoT/IIoT systems. This book delves into the synergistic potential of edge computing and advanced machine/deep learning algorithms, providing insights into lightweight and resource-efficient models with a special focus on resource-constrained edge devices. The rapidly evolving nature of cyberattacks underscores the need for updated and integrated resources that address the intersection of cybersecurity, edge computing, and deep learning. The authors address this issue by offering practical insights, lightweight models, and proactive defense mechanisms tailored to the unique challenges of securing edge devices and networks. This book is not only written to provide its audience effective strategies to detect and mitigate network intrusions by leveraging edge intelligence and advanced deep transfer learning techniques but also to provide practical insights and implementation guidelines tailored to resource-constrained edge devices. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability. Seller Inventory # 9780443382970
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